Improved Metric Distortion via Threshold Approvals

TL;DR

This paper introduces a new deterministic mechanism that uses limited cardinal information to significantly reduce metric distortion in social choice, achieving a bound of 1+√2, improving over the previous bound of 3.

Contribution

It presents a novel deterministic mechanism leveraging threshold approvals and known distances to lower distortion bounds in metric social choice.

Findings

Achieves distortion 1+√2 using threshold approvals and known distances.

Proves this bound is optimal for deterministic mechanisms in general metrics.

Provides improved bounds specifically for line metrics.

Abstract

We consider a social choice setting in which agents and alternatives are represented by points in a metric space, and the cost of an agent for an alternative is the distance between the corresponding points in the space. The goal is to choose a single alternative to (approximately) minimize the social cost (cost of all agents) or the maximum cost of any agent, when only limited information about the preferences of the agents is given. Previous work has shown that the best possible distortion one can hope to achieve is $3$ when access to the ordinal preferences of the agents is given, even when the distances between alternatives in the metric space are known. We improve upon this bound of $3$ by designing deterministic mechanisms that exploit a bit of cardinal information. We show that it is possible to achieve distortion $1+\sqrt{2}$ by using the ordinal preferences of the agents, the distances between alternatives, and a threshold approval set per agent that contains all alternatives for whom her cost is within an appropriately chosen factor of her cost for her most-preferred alternative. We show that this bound is the best possible for any deterministic mechanism in general metric spaces, and also provide improved bounds for the fundamental case of a line metric.